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What ever your metric is, its selecting all the audience for that experiment, hence the 100%

Thanks @fresh-football-47124. And what would be the 'control' in 'Chance to beat control'. The doc mentions one of the variants . However, both variants are performing the same. Where is the 77% 'chance to beat control' coming from?

have to check with @luke tomorrow

@fresh-football-47124 please let me know when you hear back from @helpful-application-7107 https://growthbookusers.slack.com/archives/C01T6PKD9C3/p1690523055491989?thread_ts=1690520924.045089&cid=C01T6PKD9C3

The reason for this is that 950/950 is more evidence for 100% than 208/208 is in a Bayesian context. We're more certain that the TG value here is actually 100% because we have more data in that group. This is one way that bayesian posteriors will differ from frequentist difference in means tests.

Hi Team During experiment analysis, I am getting the 'Sample ratio mismatch' when running a target vs control experiment . Our user base is extremely large and hence we take only a very small %age of users in our global control group ( in absolute terms which still is a few hundred thousands). Do you think this ratio mismatch would affect the results when using a bayesian engine for analysis?

What P value did you get?

@fresh-football-47124 it shows, p-value = 0.